pburg {gsignal} | R Documentation |
Autoregressive PSD estimate - Burg's method
Description
Calculate Burg maximum-entropy power spectral density.
Usage
pburg(
x,
p,
criterion = NULL,
freq = 256,
fs = 1,
range = NULL,
method = if (length(freq) == 1 && bitwAnd(freq, freq - 1) == 0) "fft" else "poly"
)
Arguments
x |
input data, specified as a numeric or complex vector or matrix. In case of a vector it represents a single signal; in case of a matrix each column is a signal. |
p |
model order; number of poles in the AR model or limit to the number of poles if a valid criterion is provided. Must be < length(x) - 2. |
criterion |
model-selection criterion. Limits the number of poles so that spurious poles are not added when the whitened data has no more information in it. Recognized values are:
The default is to NOT use a model-selection criterion (NULL) |
freq |
vector of frequencies at which power spectral density is calculated, or a scalar indicating the number of uniformly distributed frequency values at which spectral density is calculated. Default: 256. |
fs |
sampling frequency (Hz). Default: 1 |
range |
character string. one of:
Default: If model coefficients |
method |
method used to calculate the power spectral density, either
|
Value
An object of class "ar_psd" , which is a list containing two
elements, freq
and psd
containing the frequency values and
the estimates of power-spectral density, respectively.
Note
This function is a wrapper for arburg
and ar_psd
.
Author(s)
Peter V. Lanspeary, pvl@mecheng.adelaide.edu.au.
Conversion to R by Geert van Boxtel, gjmvanboxtel@gmail.com
See Also
Examples
A <- Arma(1, c(1, -2.7607, 3.8106, -2.6535, 0.9238))
y <- filter(A, 0.2 * rnorm(1024))
plot(pb <- pburg(y, 4))